Learning what's available on the web that matches one's preferences is generally considered to be useful. Sharing favorites or preferences with people is useful. Since what constitutes a favorite is very personal, recommendations from someone whose favorites more closely match your favorites is especially useful. Many times a friend tells you about some great movie, you often find you don't like it. Just because they are a friend, doesn't mean they like the same things you do. However, there are people out there who do have preferences very similar to yours. You just don't know who they are.
A single website that keeps track of user preferences (like amazon.com or youtube.com) has a database on which to draw in order to offer the capability for “people who liked this also liked . . . ” or the even more focused, “people who in general like what you like, also like . . . ”. Amazon chooses to offer such a functionality while at the time of this writing, Youtube does not. Regardless, when a particular website offers this preference matching functionality, it ONLY does it within that website. Some websites like yelp.com do not offer items for sale or download, but focus specifically on reviews for products and/or services. At present, user/visitor preferences for websites are useful in determining preference associations among users, however again, the ability to match preferences and suggest other items that the visitor might like is only available for items on that particular website and does not span multiple websites.
Determining suggestions based on an analysis of user preferences is based on a process generally known in the art as Collaborative Filtering (CF). According to Wikipedia.com, “this is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets. Collaborative filtering methods have been applied to many different kinds of data including sensing and monitoring data—such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data—such as financial service institutions that integrate many financial sources; or in electronic commerce and web 2.0 applications where the focus is on user data, etc.” “The method of making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating). The underlying assumption of CF approach is that those who agreed in the past tend to agree again in the future. For example, a collaborative filtering or recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user's tastes (likes or dislikes). Note that these predictions are specific to the user, but use information gleaned from many users.”
Today, the ability to match preferences and suggest other items that the visitor might like does not span multiple websites. Thus, it would be novel and advantageous to offer a preference matching and suggestion capability that spans the breadth of the Internet—covering all sites offering a specific type of item (videos, books, services, restaurants, etc.) or alternately covering multiple item categories. With such a capability, users would benefit from a higher degree of correlation and thus would make more informed decisions on products and services they buy.